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Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression
  1. Liliana Laranjo1,2,
  2. Ding Ding3,
  3. Bruno Heleno4,
  4. Baki Kocaballi2,5,
  5. Juan C Quiroz2,6,
  6. Huong Ly Tong2,
  7. Bahia Chahwan2,
  8. Ana Luisa Neves7,
  9. Elia Gabarron8,
  10. Kim Phuong Dao2,
  11. David Rodrigues9,
  12. Gisela Costa Neves9,
  13. Maria L Antunes10,
  14. Enrico Coiera2,
  15. David W Bates11
  1. 1Faculty of Medicine and Health, Westmead Applied Research Centre, The University of Sydney, Sydney, New South Wales, Australia
  2. 2Centre for Health Informatics - Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
  3. 3Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
  4. 4CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Lisbon, Portugal
  5. 5Faculty of Engineering and IT, University of Technology Sydney, Sydney, New South Wales, Australia
  6. 6Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia
  7. 7Institute of Global Health Innovation, Imperial College London, London, UK
  8. 8Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromso, Norway
  9. 9Nova Medical School, Universidade Nova de Lisboa, Lisboa, Portugal
  10. 10Escola Superior Tecnologias da Saude, Instituto Politécnico de Lisboa, Lisboa, Portugal
  11. 11Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
  1. Correspondence to Dr Liliana Laranjo, Faculty of Medicine and Health - Westmead Applied Research Centre, The University of Sydney, Sydney, NSW 2113, Australia; liliana.Laranjo{at}


Objective To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.

Design Systematic review and meta-analysis.

Data sources PubMed and seven additional databases, from 2007 to 2020.

Study selection Randomised controlled trials in adults (18–65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted.

Data extraction and synthesis We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses.

Main outcome measures Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents.

Results Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I2=69%, T2=0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression.

Conclusion Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.

  • physical activity
  • app
  • accelerometer
  • behaviour
  • effectiveness

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  • Twitter @LilianaLaranjo, @DrMelodyDing

  • DD and BH contributed equally.

  • Contributors The corresponding author (LL) attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. LL is the guarantor. Conception and design of the work: LL; database searching: LL, BC, MLA; title, abstract and full-text screening: LL, BC, BH, DR, ALN, EG, HLT, MLA; outcome data extraction: LL, BH; BCT coding: LL, EG, HLT; risk of bias: LL, BH, ALN; figure 1: BC, LL; figures 2 and 3: BK, LL; data analysis and interpretation: LL, BH, DD, DWB, JCQ, EC, DR, HLT, KPD, GCN; first draft: LL; critical revision of drafts for important intellectual content: LL, DD, DWB, BH, BK, JCQ, HLT, KPD, DR, EG, ALN, GCN MLA, BC, EC; final approval of the version to be published: LL, DD, DWB, BH, BK, JCQ, HLT, KPD, DR, EG, ALN, GCN, MLA, BC, EC.

  • Funding This research was supported by the National Health and Medical Research Council (grant APP1134919, Centre for Research Excellence in Digital Health). The funder had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; and preparation, review or approval of the manuscript.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement The study-specific summary data included in the meta-analysis can be obtained from the corresponding author (

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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